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Abstract

Most computer vision systems require accurate three-dimensional models. The problem of building such models from observations consists in taking multiple range image of the object from different viewing positions and orientations, referred to as "viewing poses", to match the data in the different images in order to recover the relative poses, and to merge the data into a single model using the estimated poses. The approaches proposed so far suffer from two major limitations. First, they require accurate knowledge of the relative viewing poses. Second, they either require a complicated feature extraction algorithm to be applied to the range image or they restrict the class of shapes that can be modelled. Our goal in this paper is to eliminate these two restrictions in order to allow modelling of natural, free-form objects from arbitrary unknown viewpoints.